Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations567
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory512.6 KiB
Average record size in memory925.8 B

Variable types

Text2
DateTime2
Categorical11
Numeric11

Alerts

cluster_k5 has constant value "1" Constant
antiguedad is highly overall correlated with periodo_preinscripcionHigh correlation
cant_Apoderado is highly overall correlated with cant_representante and 1 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_apercibimientos and 1 other fieldsHigh correlation
cant_apercibimientos is highly overall correlated with cant_antecedentes and 1 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_noAutenticadoHigh correlation
cant_noAutenticado is highly overall correlated with cant_apercibimientos and 1 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with cant_ApoderadoHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_ApoderadoHigh correlation
cant_suspensiones is highly overall correlated with cant_antecedentesHigh correlation
dtotal_articulos_provee is highly overall correlated with total_articulos_proveeHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
periodo_preinscripcion is highly overall correlated with antiguedadHigh correlation
total_articulos_provee is highly overall correlated with dtotal_articulos_proveeHigh correlation
Estado is highly imbalanced (60.5%) Imbalance
provincia is highly imbalanced (51.7%) Imbalance
cant_apercibimientos is highly imbalanced (96.6%) Imbalance
cant_suspensiones is highly imbalanced (97.6%) Imbalance
cant_antecedentes is highly imbalanced (95.0%) Imbalance
cant_MontoLimite is highly imbalanced (91.5%) Imbalance
CUIT has unique values Unique
antiguedad has 33 (5.8%) zeros Zeros
cant_socios has 34 (6.0%) zeros Zeros
cant_representante has 201 (35.4%) zeros Zeros
cant_noAutenticado has 214 (37.7%) zeros Zeros

Reproduction

Analysis started2025-07-08 14:18:53.396253
Analysis finished2025-07-08 14:19:04.080897
Duration10.68 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size42.1 KiB
2025-07-08T11:19:04.195184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length11
Mean length10.998236
Min length9

Characters and Unicode

Total characters6236
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique567 ?
Unique (%)100.0%

Sample

1st row33714924619
2nd row30678221976
3rd row30500106316
4th row30708326611
5th row30707327045
ValueCountFrequency (%)
30710742886 1
 
0.2%
30674562620 1
 
0.2%
33714924619 1
 
0.2%
30678221976 1
 
0.2%
30500106316 1
 
0.2%
30708326611 1
 
0.2%
30707327045 1
 
0.2%
30506333446 1
 
0.2%
30708233028 1
 
0.2%
30546666561 1
 
0.2%
Other values (557) 557
98.2%
2025-07-08T11:19:04.383399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1121
18.0%
3 991
15.9%
7 674
10.8%
6 588
9.4%
1 556
8.9%
5 540
8.7%
9 464
7.4%
8 450
7.2%
2 431
 
6.9%
4 420
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1121
18.0%
3 991
15.9%
7 674
10.8%
6 588
9.4%
1 556
8.9%
5 540
8.7%
9 464
7.4%
8 450
7.2%
2 431
 
6.9%
4 420
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1121
18.0%
3 991
15.9%
7 674
10.8%
6 588
9.4%
1 556
8.9%
5 540
8.7%
9 464
7.4%
8 450
7.2%
2 431
 
6.9%
4 420
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1121
18.0%
3 991
15.9%
7 674
10.8%
6 588
9.4%
1 556
8.9%
5 540
8.7%
9 464
7.4%
8 450
7.2%
2 431
 
6.9%
4 420
 
6.7%

Nombre
Text

Distinct564
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size51.9 KiB
2025-07-08T11:19:04.605462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length121
Median length58
Mean length23.126984
Min length2

Characters and Unicode

Total characters13113
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique563 ?
Unique (%)99.3%

Sample

1st rowSIGNIFY ARGENTINA S.A.
2nd rowFÁBRICA ARGENTINA DE AVIONES "BRIG. SAN MARTÍN" S.A..
3rd rowLA LEY SOCIEDAD ANONIMA, EDITORA E IMPRESORA
4th rowOpción Myca S.R.L..
5th rowCLAREMONT TRADING INC S.A.
ValueCountFrequency (%)
s.a 211
 
10.5%
de 100
 
5.0%
sa 96
 
4.8%
srl 81
 
4.0%
argentina 68
 
3.4%
y 36
 
1.8%
s.r.l 34
 
1.7%
la 29
 
1.4%
sociedad 16
 
0.8%
servicios 15
 
0.7%
Other values (969) 1326
65.9%
2025-07-08T11:19:04.886725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1445
 
11.0%
A 1254
 
9.6%
S 916
 
7.0%
E 692
 
5.3%
I 684
 
5.2%
R 642
 
4.9%
. 612
 
4.7%
O 560
 
4.3%
N 535
 
4.1%
C 447
 
3.4%
Other values (70) 5326
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1445
 
11.0%
A 1254
 
9.6%
S 916
 
7.0%
E 692
 
5.3%
I 684
 
5.2%
R 642
 
4.9%
. 612
 
4.7%
O 560
 
4.3%
N 535
 
4.1%
C 447
 
3.4%
Other values (70) 5326
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1445
 
11.0%
A 1254
 
9.6%
S 916
 
7.0%
E 692
 
5.3%
I 684
 
5.2%
R 642
 
4.9%
. 612
 
4.7%
O 560
 
4.3%
N 535
 
4.1%
C 447
 
3.4%
Other values (70) 5326
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1445
 
11.0%
A 1254
 
9.6%
S 916
 
7.0%
E 692
 
5.3%
I 684
 
5.2%
R 642
 
4.9%
. 612
 
4.7%
O 560
 
4.3%
N 535
 
4.1%
C 447
 
3.4%
Other values (70) 5326
40.6%
Distinct390
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2016-01-09 00:00:00
Maximum2022-11-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T11:19:04.952252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:05.061959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Imbalance 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size43.5 KiB
Inscripto
457 
Desactualizado Por Documentos Vencidos
 
41
Desactualizado Por Mantencion Formulario
 
38
Pre Inscripto
 
15
Desactualizado Por Clase
 
9
Other values (2)
 
7

Length

Max length40
Median length9
Mean length13.610229
Min length9

Characters and Unicode

Total characters7717
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 457
80.6%
Desactualizado Por Documentos Vencidos 41
 
7.2%
Desactualizado Por Mantencion Formulario 38
 
6.7%
Pre Inscripto 15
 
2.6%
Desactualizado Por Clase 9
 
1.6%
En Evaluacion 4
 
0.7%
Con Solicitud De Baja 3
 
0.5%

Length

2025-07-08T11:19:05.140008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:05.244031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 472
55.5%
desactualizado 88
 
10.4%
por 88
 
10.4%
documentos 41
 
4.8%
vencidos 41
 
4.8%
mantencion 38
 
4.5%
formulario 38
 
4.5%
pre 15
 
1.8%
clase 9
 
1.1%
en 4
 
0.5%
Other values (5) 16
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o 895
11.6%
c 687
8.9%
i 687
8.9%
n 679
8.8%
r 651
8.4%
s 651
8.4%
t 642
8.3%
I 472
 
6.1%
p 472
 
6.1%
a 363
 
4.7%
Other values (18) 1518
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7717
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 895
11.6%
c 687
8.9%
i 687
8.9%
n 679
8.8%
r 651
8.4%
s 651
8.4%
t 642
8.3%
I 472
 
6.1%
p 472
 
6.1%
a 363
 
4.7%
Other values (18) 1518
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7717
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 895
11.6%
c 687
8.9%
i 687
8.9%
n 679
8.8%
r 651
8.4%
s 651
8.4%
t 642
8.3%
I 472
 
6.1%
p 472
 
6.1%
a 363
 
4.7%
Other values (18) 1518
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7717
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 895
11.6%
c 687
8.9%
i 687
8.9%
n 679
8.8%
r 651
8.4%
s 651
8.4%
t 642
8.3%
I 472
 
6.1%
p 472
 
6.1%
a 363
 
4.7%
Other values (18) 1518
19.7%

TipoSocietario
Categorical

Distinct9
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
Sociedad Anónima
345 
Sociedad Responsabilidad Limitada
122 
Otras Formas Societarias
 
33
Organismo Publico
 
21
Cooperativas
 
17
Other values (4)
 
29

Length

Max length40
Median length16
Mean length20.446208
Min length12

Characters and Unicode

Total characters11593
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSociedad Anónima
2nd rowSociedad Anónima
3rd rowSociedad Anónima
4th rowSociedad Responsabilidad Limitada
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 345
60.8%
Sociedad Responsabilidad Limitada 122
 
21.5%
Otras Formas Societarias 33
 
5.8%
Organismo Publico 21
 
3.7%
Cooperativas 17
 
3.0%
Persona Física 13
 
2.3%
Persona Jurídica Extranjero Sin Sucursal 8
 
1.4%
Sociedades De Hecho 4
 
0.7%
Unión Transitoria de Empresas 4
 
0.7%

Length

2025-07-08T11:19:05.337771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:05.400270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 467
35.7%
anónima 345
26.4%
responsabilidad 122
 
9.3%
limitada 122
 
9.3%
otras 33
 
2.5%
formas 33
 
2.5%
societarias 33
 
2.5%
organismo 21
 
1.6%
publico 21
 
1.6%
persona 21
 
1.6%
Other values (12) 90
 
6.9%

Most occurring characters

ValueCountFrequency (%)
a 1561
13.5%
i 1470
12.7%
d 1320
11.4%
n 882
 
7.6%
o 772
 
6.7%
741
 
6.4%
e 692
 
6.0%
c 558
 
4.8%
m 525
 
4.5%
S 520
 
4.5%
Other values (27) 2552
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1561
13.5%
i 1470
12.7%
d 1320
11.4%
n 882
 
7.6%
o 772
 
6.7%
741
 
6.4%
e 692
 
6.0%
c 558
 
4.8%
m 525
 
4.5%
S 520
 
4.5%
Other values (27) 2552
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1561
13.5%
i 1470
12.7%
d 1320
11.4%
n 882
 
7.6%
o 772
 
6.7%
741
 
6.4%
e 692
 
6.0%
c 558
 
4.8%
m 525
 
4.5%
S 520
 
4.5%
Other values (27) 2552
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1561
13.5%
i 1470
12.7%
d 1320
11.4%
n 882
 
7.6%
o 772
 
6.7%
741
 
6.4%
e 692
 
6.0%
c 558
 
4.8%
m 525
 
4.5%
S 520
 
4.5%
Other values (27) 2552
22.0%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct68
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201751.22
Minimum201607
Maximum202211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:05.509632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201609.3
Q1201701
median201706
Q3201805
95-th percentile202102
Maximum202211
Range604
Interquartile range (IQR)104

Descriptive statistics

Standard deviation141.62314
Coefficient of variation (CV)0.00070196917
Kurtosis1.8152432
Mean201751.22
Median Absolute Deviation (MAD)95
Skewness1.4915762
Sum1.1439294 × 108
Variance20057.113
MonotonicityNot monotonic
2025-07-08T11:19:05.618913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 48
 
8.5%
201701 35
 
6.2%
201703 35
 
6.2%
201705 30
 
5.3%
201612 28
 
4.9%
201704 27
 
4.8%
201610 26
 
4.6%
201706 23
 
4.1%
201708 19
 
3.4%
201707 19
 
3.4%
Other values (58) 277
48.9%
ValueCountFrequency (%)
201607 3
 
0.5%
201608 14
 
2.5%
201609 12
 
2.1%
201610 26
4.6%
201611 48
8.5%
201612 28
4.9%
201701 35
6.2%
201702 17
 
3.0%
201703 35
6.2%
201704 27
4.8%
ValueCountFrequency (%)
202211 2
0.4%
202210 3
0.5%
202209 2
0.4%
202208 1
 
0.2%
202206 1
 
0.2%
202205 2
0.4%
202204 1
 
0.2%
202201 1
 
0.2%
202112 1
 
0.2%
202111 4
0.7%
Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2016-01-01 00:00:00
Maximum2022-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T11:19:05.681406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:05.759522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.64903
Minimum0
Maximum365
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:05.837639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q313
95-th percentile64.4
Maximum365
Range365
Interquartile range (IQR)12

Descriptive statistics

Standard deviation32.240789
Coefficient of variation (CV)2.2008822
Kurtosis43.859002
Mean14.64903
Median Absolute Deviation (MAD)3
Skewness5.6714097
Sum8306
Variance1039.4685
MonotonicityNot monotonic
2025-07-08T11:19:05.932014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 146
25.7%
2 73
12.9%
3 50
 
8.8%
4 32
 
5.6%
6 22
 
3.9%
5 21
 
3.7%
7 17
 
3.0%
11 14
 
2.5%
12 12
 
2.1%
8 11
 
1.9%
Other values (67) 169
29.8%
ValueCountFrequency (%)
0 2
 
0.4%
1 146
25.7%
2 73
12.9%
3 50
 
8.8%
4 32
 
5.6%
5 21
 
3.7%
6 22
 
3.9%
7 17
 
3.0%
8 11
 
1.9%
9 10
 
1.8%
ValueCountFrequency (%)
365 1
0.2%
291 1
0.2%
267 1
0.2%
189 1
0.2%
164 1
0.2%
153 1
0.2%
141 1
0.2%
136 2
0.4%
135 1
0.2%
131 1
0.2%

monto_total_adjudicado
Real number (ℝ)

High correlation 

Distinct560
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6864954 × 108
Minimum0
Maximum4.617215 × 1010
Zeros5
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:06.043394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60121.764
Q12451421.4
median16616320
Q31.0875438 × 108
95-th percentile1.4363911 × 109
Maximum4.617215 × 1010
Range4.617215 × 1010
Interquartile range (IQR)1.0630296 × 108

Descriptive statistics

Standard deviation2.5965905 × 109
Coefficient of variation (CV)5.5405805
Kurtosis185.2608
Mean4.6864954 × 108
Median Absolute Deviation (MAD)16376320
Skewness12.145801
Sum2.6572429 × 1011
Variance6.7422824 × 1018
MonotonicityNot monotonic
2025-07-08T11:19:06.137131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.9%
1419000 4
 
0.7%
1237315257 1
 
0.2%
1354243492 1
 
0.2%
3226132956 1
 
0.2%
305998 1
 
0.2%
2026657207 1
 
0.2%
184804026.6 1
 
0.2%
248156923.7 1
 
0.2%
87431445.35 1
 
0.2%
Other values (550) 550
97.0%
ValueCountFrequency (%)
0 5
0.9%
1.7 1
 
0.2%
36.13714286 1
 
0.2%
2240 1
 
0.2%
2412.02 1
 
0.2%
6218 1
 
0.2%
8544.685714 1
 
0.2%
8706.305085 1
 
0.2%
10837.5 1
 
0.2%
13246.21622 1
 
0.2%
ValueCountFrequency (%)
4.617215015 × 10101
0.2%
2.22605735 × 10101
0.2%
1.917565525 × 10101
0.2%
1.397951455 × 10101
0.2%
1.219233471 × 10101
0.2%
1.12638198 × 10101
0.2%
1.004316276 × 10101
0.2%
7675861678 1
0.2%
6782343325 1
0.2%
6458785714 1
0.2%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5784832
Minimum0
Maximum5
Zeros33
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:06.195983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3569983
Coefficient of variation (CV)0.37921046
Kurtosis0.97148995
Mean3.5784832
Median Absolute Deviation (MAD)1
Skewness-1.2733138
Sum2029
Variance1.8414443
MonotonicityNot monotonic
2025-07-08T11:19:06.259186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 255
45.0%
5 131
23.1%
3 91
 
16.0%
1 33
 
5.8%
0 33
 
5.8%
2 24
 
4.2%
ValueCountFrequency (%)
0 33
 
5.8%
1 33
 
5.8%
2 24
 
4.2%
3 91
 
16.0%
4 255
45.0%
5 131
23.1%
ValueCountFrequency (%)
5 131
23.1%
4 255
45.0%
3 91
 
16.0%
2 24
 
4.2%
1 33
 
5.8%
0 33
 
5.8%

provincia
Categorical

Imbalance 

Distinct23
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size68.8 KiB
Ciudad Autónoma de Buenos Aires
343 
Buenos Aires
94 
Santa Fe
 
29
Córdoba
 
21
Mendoza
 
18
Other values (18)
62 

Length

Max length31
Median length31
Mean length22.587302
Min length5

Characters and Unicode

Total characters12807
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.1%

Sample

1st rowBuenos Aires
2nd rowCórdoba
3rd rowCiudad Autónoma de Buenos Aires
4th rowCiudad Autónoma de Buenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 343
60.5%
Buenos Aires 94
 
16.6%
Santa Fe 29
 
5.1%
Córdoba 21
 
3.7%
Mendoza 18
 
3.2%
Neuquén 11
 
1.9%
Extranjera 8
 
1.4%
Entre Rios 6
 
1.1%
Jujuy 5
 
0.9%
Tucumán 5
 
0.9%
Other values (13) 27
 
4.8%

Length

2025-07-08T11:19:06.321679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 437
20.9%
buenos 437
20.9%
autónoma 343
16.4%
ciudad 343
16.4%
de 343
16.4%
santa 32
 
1.5%
fe 29
 
1.4%
córdoba 21
 
1.0%
mendoza 18
 
0.9%
neuquén 11
 
0.5%
Other values (24) 77
 
3.7%

Most occurring characters

ValueCountFrequency (%)
1524
11.9%
e 1312
10.2%
u 1183
9.2%
d 1074
 
8.4%
s 891
 
7.0%
n 874
 
6.8%
o 840
 
6.6%
a 832
 
6.5%
i 803
 
6.3%
A 780
 
6.1%
Other values (29) 2694
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1524
11.9%
e 1312
10.2%
u 1183
9.2%
d 1074
 
8.4%
s 891
 
7.0%
n 874
 
6.8%
o 840
 
6.6%
a 832
 
6.5%
i 803
 
6.3%
A 780
 
6.1%
Other values (29) 2694
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1524
11.9%
e 1312
10.2%
u 1183
9.2%
d 1074
 
8.4%
s 891
 
7.0%
n 874
 
6.8%
o 840
 
6.6%
a 832
 
6.5%
i 803
 
6.3%
A 780
 
6.1%
Other values (29) 2694
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1524
11.9%
e 1312
10.2%
u 1183
9.2%
d 1074
 
8.4%
s 891
 
7.0%
n 874
 
6.8%
o 840
 
6.6%
a 832
 
6.5%
i 803
 
6.3%
A 780
 
6.1%
Other values (29) 2694
21.0%

cant_socios
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6895944
Minimum0
Maximum23
Zeros34
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:06.399059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1231388
Coefficient of variation (CV)0.78938997
Kurtosis17.318028
Mean2.6895944
Median Absolute Deviation (MAD)1
Skewness2.7983306
Sum1525
Variance4.5077184
MonotonicityNot monotonic
2025-07-08T11:19:06.463756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 149
26.3%
1 133
23.5%
3 111
19.6%
4 65
11.5%
0 34
 
6.0%
5 30
 
5.3%
6 20
 
3.5%
7 8
 
1.4%
8 6
 
1.1%
9 4
 
0.7%
Other values (5) 7
 
1.2%
ValueCountFrequency (%)
0 34
 
6.0%
1 133
23.5%
2 149
26.3%
3 111
19.6%
4 65
11.5%
5 30
 
5.3%
6 20
 
3.5%
7 8
 
1.4%
8 6
 
1.1%
9 4
 
0.7%
ValueCountFrequency (%)
23 1
 
0.2%
14 1
 
0.2%
13 1
 
0.2%
11 1
 
0.2%
10 3
 
0.5%
9 4
 
0.7%
8 6
 
1.1%
7 8
 
1.4%
6 20
3.5%
5 30
5.3%

cant_apercibimientos
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
0.0
565 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1701
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 565
99.6%
1.0 2
 
0.4%

Length

2025-07-08T11:19:06.532460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:06.569251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 565
99.6%
1.0 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
1 2
 
0.1%

cant_suspensiones
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
0.0
565 
2.0
 
1
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1701
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 565
99.6%
2.0 1
 
0.2%
1.0 1
 
0.2%

Length

2025-07-08T11:19:06.627699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:06.671996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 565
99.6%
2.0 1
 
0.2%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
2 1
 
0.1%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
2 1
 
0.1%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
2 1
 
0.1%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1132
66.5%
. 567
33.3%
2 1
 
0.1%
1 1
 
0.1%

cant_antecedentes
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
0.0
562 
1.0
 
4
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1701
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 562
99.1%
1.0 4
 
0.7%
2.0 1
 
0.2%

Length

2025-07-08T11:19:06.727081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:06.758328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 562
99.1%
1.0 4
 
0.7%
2.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1129
66.4%
. 567
33.3%
1 4
 
0.2%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1129
66.4%
. 567
33.3%
1 4
 
0.2%
2 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1129
66.4%
. 567
33.3%
1 4
 
0.2%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1129
66.4%
. 567
33.3%
1 4
 
0.2%
2 1
 
0.1%

cant_Apoderado
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6578483
Minimum0
Maximum12
Zeros5
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:06.820821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.504776
Coefficient of variation (CV)0.56616324
Kurtosis8.4802805
Mean2.6578483
Median Absolute Deviation (MAD)1
Skewness2.2001999
Sum1507
Variance2.2643508
MonotonicityNot monotonic
2025-07-08T11:19:06.883315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 200
35.3%
3 170
30.0%
1 92
16.2%
4 58
 
10.2%
5 21
 
3.7%
7 5
 
0.9%
8 5
 
0.9%
6 5
 
0.9%
0 5
 
0.9%
10 2
 
0.4%
Other values (3) 4
 
0.7%
ValueCountFrequency (%)
0 5
 
0.9%
1 92
16.2%
2 200
35.3%
3 170
30.0%
4 58
 
10.2%
5 21
 
3.7%
6 5
 
0.9%
7 5
 
0.9%
8 5
 
0.9%
9 1
 
0.2%
ValueCountFrequency (%)
12 1
 
0.2%
11 2
 
0.4%
10 2
 
0.4%
9 1
 
0.2%
8 5
 
0.9%
7 5
 
0.9%
6 5
 
0.9%
5 21
 
3.7%
4 58
 
10.2%
3 170
30.0%

cant_representante
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93474427
Minimum0
Maximum10
Zeros201
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:06.946000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96455334
Coefficient of variation (CV)1.0318901
Kurtosis14.182941
Mean0.93474427
Median Absolute Deviation (MAD)1
Skewness2.2528791
Sum530
Variance0.93036314
MonotonicityNot monotonic
2025-07-08T11:19:06.993962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 244
43.0%
0 201
35.4%
2 98
17.3%
3 13
 
2.3%
4 9
 
1.6%
5 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
0 201
35.4%
1 244
43.0%
2 98
17.3%
3 13
 
2.3%
4 9
 
1.6%
5 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
5 1
 
0.2%
4 9
 
1.6%
3 13
 
2.3%
2 98
17.3%
1 244
43.0%
0 201
35.4%

cant_autenticado
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2962963
Minimum0
Maximum11
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:07.041199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2625499
Coefficient of variation (CV)0.54982011
Kurtosis8.92239
Mean2.2962963
Median Absolute Deviation (MAD)1
Skewness1.8970212
Sum1302
Variance1.5940322
MonotonicityNot monotonic
2025-07-08T11:19:07.103682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 179
31.6%
1 175
30.9%
2 152
26.8%
4 37
 
6.5%
5 15
 
2.6%
6 4
 
0.7%
11 2
 
0.4%
9 1
 
0.2%
8 1
 
0.2%
0 1
 
0.2%
ValueCountFrequency (%)
0 1
 
0.2%
1 175
30.9%
2 152
26.8%
3 179
31.6%
4 37
 
6.5%
5 15
 
2.6%
6 4
 
0.7%
8 1
 
0.2%
9 1
 
0.2%
11 2
 
0.4%
ValueCountFrequency (%)
11 2
 
0.4%
9 1
 
0.2%
8 1
 
0.2%
6 4
 
0.7%
5 15
 
2.6%
4 37
 
6.5%
3 179
31.6%
2 152
26.8%
1 175
30.9%
0 1
 
0.2%

cant_noAutenticado
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2962963
Minimum0
Maximum12
Zeros214
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:07.180476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4957078
Coefficient of variation (CV)1.1538317
Kurtosis8.5332698
Mean1.2962963
Median Absolute Deviation (MAD)1
Skewness2.1522263
Sum735
Variance2.2371417
MonotonicityNot monotonic
2025-07-08T11:19:07.247296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 214
37.7%
2 136
24.0%
1 133
23.5%
3 56
 
9.9%
6 8
 
1.4%
5 8
 
1.4%
4 7
 
1.2%
10 1
 
0.2%
9 1
 
0.2%
7 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
0 214
37.7%
1 133
23.5%
2 136
24.0%
3 56
 
9.9%
4 7
 
1.2%
5 8
 
1.4%
6 8
 
1.4%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
12 1
 
0.2%
10 1
 
0.2%
9 1
 
0.2%
8 1
 
0.2%
7 1
 
0.2%
6 8
 
1.4%
5 8
 
1.4%
4 7
 
1.2%
3 56
9.9%
2 136
24.0%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5820106
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:07.302413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q34
95-th percentile6
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3278323
Coefficient of variation (CV)0.3706947
Kurtosis17.014367
Mean3.5820106
Median Absolute Deviation (MAD)0
Skewness3.5123628
Sum2031
Variance1.7631387
MonotonicityNot monotonic
2025-07-08T11:19:07.382461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 355
62.6%
4 120
 
21.2%
5 38
 
6.7%
2 20
 
3.5%
6 14
 
2.5%
7 7
 
1.2%
8 4
 
0.7%
12 3
 
0.5%
11 2
 
0.4%
9 1
 
0.2%
Other values (3) 3
 
0.5%
ValueCountFrequency (%)
1 1
 
0.2%
2 20
 
3.5%
3 355
62.6%
4 120
 
21.2%
5 38
 
6.7%
6 14
 
2.5%
7 7
 
1.2%
8 4
 
0.7%
9 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
13 1
 
0.2%
12 3
 
0.5%
11 2
 
0.4%
10 1
 
0.2%
9 1
 
0.2%
8 4
 
0.7%
7 7
 
1.2%
6 14
 
2.5%
5 38
 
6.7%
4 120
21.2%

cant_MontoLimite
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
0.0
561 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1701
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 561
98.9%
1.0 6
 
1.1%

Length

2025-07-08T11:19:07.446139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:07.493089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 561
98.9%
1.0 6
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1128
66.3%
. 567
33.3%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1128
66.3%
. 567
33.3%
1 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1128
66.3%
. 567
33.3%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1128
66.3%
. 567
33.3%
1 6
 
0.4%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct130
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.132275
Minimum1
Maximum1732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-07-08T11:19:07.555573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q334
95-th percentile168.9
Maximum1732
Range1731
Interquartile range (IQR)31

Descriptive statistics

Standard deviation120.06983
Coefficient of variation (CV)2.7206808
Kurtosis81.786283
Mean44.132275
Median Absolute Deviation (MAD)8
Skewness7.6244973
Sum25023
Variance14416.765
MonotonicityNot monotonic
2025-07-08T11:19:07.633687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 85
 
15.0%
2 49
 
8.6%
3 34
 
6.0%
4 31
 
5.5%
5 22
 
3.9%
7 20
 
3.5%
9 17
 
3.0%
10 17
 
3.0%
8 14
 
2.5%
6 13
 
2.3%
Other values (120) 265
46.7%
ValueCountFrequency (%)
1 85
15.0%
2 49
8.6%
3 34
 
6.0%
4 31
 
5.5%
5 22
 
3.9%
6 13
 
2.3%
7 20
 
3.5%
8 14
 
2.5%
9 17
 
3.0%
10 17
 
3.0%
ValueCountFrequency (%)
1732 1
0.2%
850 1
0.2%
801 1
0.2%
780 1
0.2%
773 1
0.2%
636 1
0.2%
600 1
0.2%
577 1
0.2%
563 1
0.2%
351 1
0.2%
Distinct20
Distinct (%)3.5%
Missing2
Missing (%)0.4%
Memory size50.4 KiB
(222964579.98, 46172150151.0]
96 
(89439449.702, 222964579.98]
60 
(13557176.81, 19975532.58]
40 
(46718747.516, 89439449.702]
39 
(30451916.51, 46718747.516]
37 
Other values (15)
293 

Length

Max length29
Median length28
Mean length26.086726
Min length19

Characters and Unicode

Total characters14739
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(13557176.81, 19975532.58]
2nd row(222964579.98, 46172150151.0]
3rd row(222964579.98, 46172150151.0]
4th row(89439449.702, 222964579.98]
5th row(46718747.516, 89439449.702]

Common Values

ValueCountFrequency (%)
(222964579.98, 46172150151.0] 96
16.9%
(89439449.702, 222964579.98] 60
 
10.6%
(13557176.81, 19975532.58] 40
 
7.1%
(46718747.516, 89439449.702] 39
 
6.9%
(30451916.51, 46718747.516] 37
 
6.5%
(6702697.888, 9424898.401] 31
 
5.5%
(19975532.58, 30451916.51] 30
 
5.3%
(9424898.401, 13557176.81] 28
 
4.9%
(2483085.385, 3396600.0] 24
 
4.2%
(3396600.0, 4727330.113] 22
 
3.9%
Other values (10) 158
27.9%

Length

2025-07-08T11:19:07.733757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
222964579.98 156
13.8%
89439449.702 99
 
8.8%
46172150151.0 96
 
8.5%
46718747.516 76
 
6.7%
19975532.58 70
 
6.2%
13557176.81 68
 
6.0%
30451916.51 67
 
5.9%
9424898.401 59
 
5.2%
6702697.888 48
 
4.2%
3396600.0 46
 
4.1%
Other values (11) 345
30.5%

Most occurring characters

ValueCountFrequency (%)
1 1499
10.2%
9 1402
9.5%
7 1294
8.8%
5 1231
 
8.4%
. 1130
 
7.7%
4 1091
 
7.4%
2 1081
 
7.3%
8 1037
 
7.0%
0 939
 
6.4%
3 902
 
6.1%
Other values (6) 3133
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1499
10.2%
9 1402
9.5%
7 1294
8.8%
5 1231
 
8.4%
. 1130
 
7.7%
4 1091
 
7.4%
2 1081
 
7.3%
8 1037
 
7.0%
0 939
 
6.4%
3 902
 
6.1%
Other values (6) 3133
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1499
10.2%
9 1402
9.5%
7 1294
8.8%
5 1231
 
8.4%
. 1130
 
7.7%
4 1091
 
7.4%
2 1081
 
7.3%
8 1037
 
7.0%
0 939
 
6.4%
3 902
 
6.1%
Other values (6) 3133
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1499
10.2%
9 1402
9.5%
7 1294
8.8%
5 1231
 
8.4%
. 1130
 
7.7%
4 1091
 
7.4%
2 1081
 
7.3%
8 1037
 
7.0%
0 939
 
6.4%
3 902
 
6.1%
Other values (6) 3133
21.3%
Distinct10
Distinct (%)1.8%
Missing2
Missing (%)0.4%
Memory size42.4 KiB
(0.999, 2.0]
219 
(39.0, 1214.0]
55 
(2.0, 3.0]
50 
(12.0, 19.0]
48 
(8.0, 12.0]
46 
Other values (5)
147 

Length

Max length14
Median length12
Mean length11.571681
Min length10

Characters and Unicode

Total characters6538
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(0.999, 2.0]
2nd row(39.0, 1214.0]
3rd row(39.0, 1214.0]
4th row(8.0, 12.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 219
38.6%
(39.0, 1214.0] 55
 
9.7%
(2.0, 3.0] 50
 
8.8%
(12.0, 19.0] 48
 
8.5%
(8.0, 12.0] 46
 
8.1%
(19.0, 39.0] 44
 
7.8%
(3.0, 4.0] 32
 
5.6%
(6.0, 8.0] 28
 
4.9%
(5.0, 6.0] 22
 
3.9%
(4.0, 5.0] 21
 
3.7%
(Missing) 2
 
0.4%

Length

2025-07-08T11:19:07.813826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:07.886839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 269
23.8%
0.999 219
19.4%
39.0 99
 
8.8%
12.0 94
 
8.3%
19.0 92
 
8.1%
3.0 82
 
7.3%
8.0 74
 
6.5%
1214.0 55
 
4.9%
4.0 53
 
4.7%
6.0 50
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 1130
17.3%
. 1130
17.3%
9 848
13.0%
( 565
8.6%
, 565
8.6%
565
8.6%
] 565
8.6%
2 418
 
6.4%
1 296
 
4.5%
3 181
 
2.8%
Other values (4) 275
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1130
17.3%
. 1130
17.3%
9 848
13.0%
( 565
8.6%
, 565
8.6%
565
8.6%
] 565
8.6%
2 418
 
6.4%
1 296
 
4.5%
3 181
 
2.8%
Other values (4) 275
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1130
17.3%
. 1130
17.3%
9 848
13.0%
( 565
8.6%
, 565
8.6%
565
8.6%
] 565
8.6%
2 418
 
6.4%
1 296
 
4.5%
3 181
 
2.8%
Other values (4) 275
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1130
17.3%
. 1130
17.3%
9 848
13.0%
( 565
8.6%
, 565
8.6%
565
8.6%
] 565
8.6%
2 418
 
6.4%
1 296
 
4.5%
3 181
 
2.8%
Other values (4) 275
 
4.2%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size42.4 KiB
(0.999, 2.0]
134 
(8.0, 11.0]
45 
(97.6, 161.0]
36 
(4.0, 6.0]
35 
(21.0, 29.0]
35 
Other values (10)
282 

Length

Max length15
Median length12
Mean length11.640212
Min length10

Characters and Unicode

Total characters6600
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(0.999, 2.0]
2nd row(11.0, 15.0]
3rd row(2.0, 3.0]
4th row(3.0, 4.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 134
23.6%
(8.0, 11.0] 45
 
7.9%
(97.6, 161.0] 36
 
6.3%
(4.0, 6.0] 35
 
6.2%
(21.0, 29.0] 35
 
6.2%
(6.0, 8.0] 34
 
6.0%
(2.0, 3.0] 34
 
6.0%
(29.0, 40.0] 33
 
5.8%
(15.0, 21.0] 32
 
5.6%
(3.0, 4.0] 31
 
5.5%
Other values (5) 118
20.8%

Length

2025-07-08T11:19:07.985817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 168
14.8%
0.999 134
11.8%
8.0 79
 
7.0%
11.0 76
 
6.7%
6.0 69
 
6.1%
29.0 68
 
6.0%
21.0 67
 
5.9%
4.0 66
 
5.8%
3.0 65
 
5.7%
97.6 65
 
5.7%
Other values (6) 277
24.4%

Most occurring characters

ValueCountFrequency (%)
. 1134
17.2%
0 1129
17.1%
( 567
8.6%
, 567
8.6%
567
8.6%
] 567
8.6%
9 557
8.4%
1 394
 
6.0%
2 303
 
4.6%
6 201
 
3.0%
Other values (5) 614
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1134
17.2%
0 1129
17.1%
( 567
8.6%
, 567
8.6%
567
8.6%
] 567
8.6%
9 557
8.4%
1 394
 
6.0%
2 303
 
4.6%
6 201
 
3.0%
Other values (5) 614
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1134
17.2%
0 1129
17.1%
( 567
8.6%
, 567
8.6%
567
8.6%
] 567
8.6%
9 557
8.4%
1 394
 
6.0%
2 303
 
4.6%
6 201
 
3.0%
Other values (5) 614
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1134
17.2%
0 1129
17.1%
( 567
8.6%
, 567
8.6%
567
8.6%
] 567
8.6%
9 557
8.4%
1 394
 
6.0%
2 303
 
4.6%
6 201
 
3.0%
Other values (5) 614
9.3%

cluster_k5
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size36.5 KiB
1
567 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 567
100.0%

Length

2025-07-08T11:19:08.062734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:08.094059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 567
100.0%

Most occurring characters

ValueCountFrequency (%)
1 567
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 567
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 567
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 567
100.0%

Interactions

2025-07-08T11:19:02.639596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.326478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.165461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.253170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.132152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.915336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.644837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.560941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.296112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.063026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.830055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.719369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.413013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.247564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.318855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.213232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.994499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.727465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.627405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.377455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.129735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.913250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.794970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.494938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.330082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.436914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.296417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.061751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.794074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.696111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.443825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.210605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.980613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.863606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.565752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.404367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.503068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.363601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.111592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.863345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.775764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.510919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.275326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.045563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.928310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.629538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.473736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.569737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.431893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.188071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.928403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.828686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.580009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.329913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.113667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:03.005141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.693722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.541422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.656970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.500393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.253241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.994809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.894887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.655015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.413199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.186162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:03.081677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.771836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.613983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.733911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.562422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.320427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.062025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.961607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.713231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.479792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.266834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:03.154848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.850021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.684657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.805925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.638355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.379970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.147262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.028673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.779785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.547086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.336179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:03.229833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:54.929147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.015437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.889864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.705013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.445516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.195364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.096585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.846351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.613070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.394855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:03.306349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.013834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.096750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.967793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.762475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.512123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.427869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.163874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.913045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.693888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.484727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:03.585551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:55.092389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:56.169894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.046954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:57.841918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:58.580199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:59.494386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.229104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:00.979855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:01.763141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:02.562480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-08T11:19:08.156554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_apercibimientoscant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.1810.1570.0000.0320.0000.0000.1260.0600.0000.0480.0260.0000.0000.1010.1650.0690.0000.1430.1560.000
TipoSocietario0.1811.0000.1080.1320.0000.0000.0000.1050.0580.0000.1720.0960.0830.0000.0490.0990.0900.0000.1130.3850.109
antiguedad0.1570.1081.0000.0880.0000.0000.000-0.1590.1720.410-0.0810.0650.0820.0000.1790.1760.0280.324-0.9450.0970.184
cant_Apoderado0.0000.1320.0881.0000.3570.4230.3170.1650.1780.151-0.7100.5930.0020.3910.0610.0000.0600.133-0.1280.0000.060
cant_MontoLimite0.0320.0000.0000.3571.0000.0000.0000.1960.0370.0000.0000.1700.0000.0000.0000.0000.0000.0000.0000.0000.000
cant_antecedentes0.0000.0000.0000.4230.0001.0000.7040.4890.3560.1370.0000.3830.1580.7890.1190.0000.0580.0000.0000.0000.000
cant_apercibimientos0.0000.0000.0000.3170.0000.7041.0000.0000.7450.2620.0000.3460.1170.0000.0080.0000.0810.0000.0000.0000.000
cant_autenticado0.1260.105-0.1590.1650.1960.4890.0001.000-0.802-0.023-0.0220.159-0.1360.4890.0180.0000.012-0.0080.1750.000-0.039
cant_noAutenticado0.0600.0580.1720.1780.0370.3560.745-0.8021.0000.0300.0800.3990.1200.0000.0000.0000.0510.011-0.1980.0650.028
cant_procesos_adjudicado0.0000.0000.4100.1510.0000.1370.262-0.0230.0301.000-0.1560.0530.0490.0060.3430.0500.0950.654-0.4570.0000.311
cant_representante0.0480.172-0.081-0.7100.0000.0000.000-0.0220.080-0.1561.0000.063-0.0630.0000.0000.0600.065-0.1540.1000.072-0.094
cant_sinMontoLimite0.0260.0960.0650.5930.1700.3830.3460.1590.3990.0530.0631.000-0.0130.3310.0900.0000.0590.043-0.0950.000-0.038
cant_socios0.0000.0830.0820.0020.0000.1580.117-0.1360.1200.049-0.063-0.0131.0000.0000.0000.0520.0000.092-0.1070.0000.029
cant_suspensiones0.0000.0000.0000.3910.0000.7890.0000.4890.0000.0060.0000.3310.0001.0000.0530.0000.1120.0000.0000.0000.141
dcant_procesos_adjudicado0.1010.0490.1790.0610.0000.1190.0080.0180.0000.3430.0000.0900.0000.0531.0000.2130.1270.0940.1570.0580.098
dmonto_total_adjudicado0.1650.0990.1760.0000.0000.0000.0000.0000.0000.0500.0600.0000.0520.0000.2131.0000.0000.0000.1890.0780.022
dtotal_articulos_provee0.0690.0900.0280.0600.0000.0580.0810.0120.0510.0950.0650.0590.0000.1120.1270.0001.0000.0420.0390.0000.589
monto_total_adjudicado0.0000.0000.3240.1330.0000.0000.000-0.0080.0110.654-0.1540.0430.0920.0000.0940.0000.0421.000-0.3570.0000.112
periodo_preinscripcion0.1430.113-0.945-0.1280.0000.0000.0000.175-0.198-0.4570.100-0.095-0.1070.0000.1570.1890.039-0.3571.0000.177-0.195
provincia0.1560.3850.0970.0000.0000.0000.0000.0000.0650.0000.0720.0000.0000.0000.0580.0780.0000.0000.1771.0000.000
total_articulos_provee0.0000.1090.1840.0600.0000.0000.000-0.0390.0280.311-0.094-0.0380.0290.1410.0980.0220.5890.112-0.1950.0001.000

Missing values

2025-07-08T11:19:03.737292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T11:19:03.898052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-08T11:19:04.035337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
1533714924619SIGNIFY ARGENTINA S.A.18/10/2016InscriptoSociedad Anónima20161020161.01.943875e+075.0Buenos Aires4.00.00.00.02.01.01.02.03.00.01.0(13557176.81, 19975532.58](0.999, 2.0](0.999, 2.0]1
2530678221976FÁBRICA ARGENTINA DE AVIONES "BRIG. SAN MARTÍN" S.A..17/11/2016InscriptoSociedad Anónima201611201663.01.917566e+105.0Córdoba4.00.00.00.01.00.01.00.01.00.014.0(222964579.98, 46172150151.0](39.0, 1214.0](11.0, 15.0]1
3430500106316LA LEY SOCIEDAD ANONIMA, EDITORA E IMPRESORA21/07/2016InscriptoSociedad Anónima2016072016189.02.752135e+085.0Ciudad Autónoma de Buenos Aires3.00.00.00.011.00.01.010.011.00.03.0(222964579.98, 46172150151.0](39.0, 1214.0](2.0, 3.0]1
4330708326611Opción Myca S.R.L..11/10/2016InscriptoSociedad Responsabilidad Limitada201610201611.01.629197e+085.0Ciudad Autónoma de Buenos Aires2.00.00.00.03.00.01.02.03.00.04.0(89439449.702, 222964579.98](8.0, 12.0](3.0, 4.0]1
4630707327045CLAREMONT TRADING INC S.A.05/12/2016InscriptoSociedad Anónima20161220161.05.559000e+075.0Ciudad Autónoma de Buenos Aires1.00.00.00.03.01.01.03.04.00.01.0(46718747.516, 89439449.702](0.999, 2.0](0.999, 2.0]1
4830506333446BORCAL S.A.I.C.22/09/2016InscriptoSociedad Anónima201609201627.01.237315e+095.0Ciudad Autónoma de Buenos Aires3.00.00.00.03.01.01.03.04.00.02.0(222964579.98, 46172150151.0](19.0, 39.0](0.999, 2.0]1
4930708233028TELAM SOCIEDAD DEL ESTADO05/12/2016InscriptoOrganismo Publico201612201668.01.354243e+095.0Ciudad Autónoma de Buenos Aires0.00.00.00.01.02.01.02.03.00.02.0(222964579.98, 46172150151.0](39.0, 1214.0](0.999, 2.0]1
5230546666561UNIVERSIDAD DE BUENOS AIRES21/11/2016InscriptoOrganismo Publico2016112016136.03.226133e+095.0Ciudad Autónoma de Buenos Aires0.00.00.00.09.00.09.00.09.00.01.0(222964579.98, 46172150151.0](39.0, 1214.0](0.999, 2.0]1
5330711397465livemedia srl16/11/2016Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20161120161.03.059980e+055.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.02.03.00.03.00.04.0(224078.198, 377939.298](0.999, 2.0](3.0, 4.0]1
5430585581247INVAP SOCIEDAD DEL ESTADO.25/11/2016InscriptoOtras Formas Societarias201611201627.02.026657e+095.0Rio Negro7.00.00.00.06.00.03.03.06.00.06.0(222964579.98, 46172150151.0](19.0, 39.0](4.0, 6.0]1
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
982030641976438OSCAR POURTAU S.A29/05/2017InscriptoSociedad Anónima20170520171.02.373000e+054.0Buenos Aires3.00.00.00.05.01.05.01.05.01.013.0(224078.198, 377939.298](0.999, 2.0](11.0, 15.0]1
982230711125317NETSAVIA S.A.30/06/2022InscriptoSociedad Anónima20220620221.07.936965e+060.0Ciudad Autónoma de Buenos Aires5.00.00.00.02.02.02.02.04.00.011.0(6702697.888, 9424898.401](0.999, 2.0](8.0, 11.0]1
990130658446300ALBER GUS S.A.14/03/2017InscriptoSociedad Anónima20170320171.04.718984e+064.0Córdoba3.00.00.00.02.01.03.00.03.00.07.0(3396600.0, 4727330.113](0.999, 2.0](6.0, 8.0]1
993230714864137GASFRA SRL13/07/2020InscriptoSociedad Responsabilidad Limitada20200720201.01.233871e+051.0Ciudad Autónoma de Buenos Aires2.00.00.00.02.01.03.00.03.00.029.0(104767.373, 224078.198](0.999, 2.0](21.0, 29.0]1
993330711301638MTG GROUP S.R.L.09/11/2016InscriptoSociedad Responsabilidad Limitada20161120162.01.763192e+075.0Ciudad Autónoma de Buenos Aires4.00.00.00.03.04.04.03.07.00.020.0(13557176.81, 19975532.58](0.999, 2.0](15.0, 21.0]1
994233716530529Interacción Consultora SAS25/11/2020InscriptoOtras Formas Societarias20201120201.02.194518e+061.0Mendoza3.00.00.00.03.00.03.00.03.00.03.0(1793326.755, 2483085.385](0.999, 2.0](2.0, 3.0]1
999933716048549COOPERATIVA DE TRABAJO LA MORENA LTDA27/08/2021InscriptoCooperativas20210820211.05.826644e+060.0Santa Fe8.00.00.00.02.01.01.02.03.00.09.0(4727330.113, 6702697.888](0.999, 2.0](8.0, 11.0]1
1001330715385712LENOVO GLOBAL TECHNOLOGY ARGENTINA SRL25/09/2017Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20170920171.07.136995e+064.0Buenos Aires1.00.00.00.01.02.01.02.03.00.037.0(6702697.888, 9424898.401](0.999, 2.0](29.0, 40.0]1
1002633541350999SOCIEDAD RURAL DE JESUS MARIA17/11/2021InscriptoOtras Formas Societarias20211120211.02.898882e+060.0Córdoba3.00.00.00.01.02.03.00.03.00.01.0(2483085.385, 3396600.0](0.999, 2.0](0.999, 2.0]1
1004030674562620INAR VIAL S.A.22/08/2022InscriptoSociedad Anónima20220820222.07.810812e+060.0Santa Fe5.00.00.00.01.04.01.04.05.00.01.0(6702697.888, 9424898.401](0.999, 2.0](0.999, 2.0]1